Mapping road illumination

ABSTRACT

A method and apparatus for providing navigation services including illumination data. A request for a route is received with one or more preferences related to illumination for the route. One or more road segments that have illumination road attributes that correspond to the one or more preferences are selected. The illumination road attributes are calculated as a function of high beam frequency for the one or more road segments. The route is generated including the one or more road segments. The route is provided with the illumination road attributes.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation under 35 U.S.C § 120 and 37 C.F.R. §1.53(b) of U.S. patent application Ser. No. 15/489,178 filed Apr. 17,2017, which is a continuation of U.S. patent application Ser. No.15/053,548 filed Feb. 25, 2016 now U.S. Pat. No. 9,651,390, the entiredisclosures of which are hereby incorporated by reference in theirentirety.

FIELD

The following disclosure relates to vehicular high beam usage, mapping,and navigation devices or services.

BACKGROUND

Navigation systems are used by people and vehicles for routing anddirections in order to travel between two locations. Navigation systemsrequire accurate information to properly route vehicles. Identifyingexactly where a vehicle is on the road in real time, along with itsimmediate surroundings, may eliminate many dangerous unknowns. Further,a comprehensive map is a crucial component of assisted or automaticdriving. Vehicles may include many sensors, but a comprehensive map maybe the most important tool vehicles use. Sensors in vehicles may be ableto detect lanes and lane markings in real time using image processingand Lidar based systems. These systems are useful for obstacle avoidanceand detecting the movements of other vehicles. When used alone though,on board sensor systems may exhibit large blind spots and may be unableto predict events or maneuvers even a short distance away, i.e. out ofrange of the vehicle's sensors.

On-board sensors, however, when combined with comprehensive maps mayallow for assisted and highly automated vehicle operation. Acomprehensive map and an associated geographic database may be made upof information or data observed in real-time or measurements gatheredover time. The geographic database may include information about therepresented geographic features, such as the positions of the roads,speed limits along portions of roads, address ranges along the roadportions, turn restrictions at intersections of roads, directionrestrictions, such as one-way streets, and so on. Information for thegeographic database may be collected, sorted, and analyzed in orderprovide accurate estimations for the roadway.

SUMMARY

A method comprising receiving high beam data indicative of high beam useat a location and roadway conditions for the location. A processorcalculates a high beam confidence value from the high beam data and theroadway conditions. The processor calculates a high beam frequency fromthe high beam confidence value. The processor augments a geographicdatabase to include the high beam frequency.

A method comprising receiving high beam data indicative of high beam andambient light data. A processor calculates a high beam frequency. Theprocessor calculates an ambient light value. The processor generates aroad illumination value for one or more road segments based on the highbeam frequency and the ambient light value.

An apparatus comprising a processor, data storage, road segment dataentities, and high beam data entities. The road segment data entitiesrepresent locations of a road network located in a geographic region.The high beam data entities represents high beam usage at a respectivelocation.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present invention are described herein withreference to the following drawings.

FIG. 1 illustrates an example system for mapping road illumination.

FIG. 2 illustrates a map of a geographic region.

FIG. 3 illustrates a block diagram of a geographic database of FIG. 1.

FIG. 4 illustrates a block diagram of components of data recordscontained in the geographic database of FIG. 3.

FIG. 5 illustrates an example flowchart for mapping road illumination.

FIG. 6 illustrates example high beam data.

FIG. 7 illustrates an example server of the system of FIG. 1.

FIG. 8 illustrates an example workflow for estimating roadwayillumination using the server of FIG. 7.

FIGS. 9A and 9B illustrate examples of received ambient light data.

FIG. 10 illustrates an example device of the system of FIG. 1.

DETAILED DESCRIPTION

The following embodiments relate to systems and methods for collectingand analyzing data collected from vehicles to identify and map roadwayillumination. As the collected data may include data not relevant toroadway illumination, other data coincident or contemporaneous with thecollected data may be received to help distinguish or otherwise extractor separate the relevant data from the non-relevant data.

Roads may have different levels of natural and artificial illuminationfor nighttime vehicle operation. Certain roads or road segments may belit poorer than other road and segments. The lack of illumination may becaused by random events or circumstances. For example, locations withpoor illumination may be temporarily caused by weather, other vehicles,or obstructions. However, a main cause of poor illumination is the lackof artificial lighting, e.g. overhead lighting. Overhead or otherartificial lighting that is common in city settings or populated areasmay be sparse in rural or suburban areas. Identification of areas thatare consistently under lit may be beneficial to promote safer vehicleoperation. In the case of a traditional vehicle, a maximum safe drivingspeed may be limited by the range the driver can see. In the case of anautonomous vehicle, a maximum speed may be limited by the range of thevehicle sensors. Although sensor technologies such as radar andultrasound may be unaffected by visible sight range, video cameras orother optical sensors may require adequate light to function. Videocameras are used for important tasks such as the detection of signs,identification of lane markers, etc. Identification of low illuminationareas may improve both routing and real-time vehicle safety. One methodfor identification of low illumination areas is to identify when avehicle operates with the brightest and/or longest range setting of thevehicle's headlights activated, referred to as the “brights” or “highbeams”.

Headlights operating on “upper” or on high beam allow a vehicle'soperator to detect objects further out as high beam headlights providebrighter, more directed, long-range illumination as compared with lowbeam or normal headlights. In areas where adequate overhead illuminationis not provided, high beams are often used. As such, manual and/orautomatic high beam usage by a vehicle operating in those areas mayindicate low illumination areas. High beam usage, however, is not onlyused for lack of illumination. Use of high beams may indicate othernon-illumination events such as passing vehicles or weather relatedvisibility. In order to use high beam data to identify low illuminationareas, high beam events unrelated to illumination may be excluded.

Data related to ambient lighting may be collected indirectly, such asthrough determining headlight or high beam usage, or directly throughambient light sensors. Both types of data, indirect and direct, mayinclude noise. The noise may be diminished, e.g. statistically, byanalyzing the data using functions and additional data collected inconjunction with, and/or derived from, the high-beam usage data. Thedata, once analyzed and processed, may be used to generate orsupplement/augment a geographic database, such as a geographic databasecomprising links and nodes with attributes indicating the estimatedroadway illumination. The estimated roadway illumination may be used togenerate routes, operate vehicles, or in combination with otherinformation to allow for safer and more efficient operation on theroadways.

FIG. 1 illustrates an example system for identifying and mapping roadwayillumination. The system includes one or more devices 122, a network127, and a mapping system 121. The mapping system 121 may include adatabase 116 (also referred to as a geographic database or map database)and a server 125. Additional, different, or fewer components may beincluded.

The mapping system 121 may include multiple servers, workstations,databases, and other machines connected together and maintained by a mapdeveloper. The mapping system 121 may be configured to identify orestimate ambient light available on road segments at various times ofthe day, including at night. The mapping system may also be configuredto generate routes or paths between two points (nodes) on a stored map.The mapping system 121 may be configured to provide up to dateinformation and maps to external geographic databases or mappingapplications. The mapping system 121 may be configured to encode ordecode map or geographic data. The identified roadway illuminationestimations may be stored by the mapping system 121 in the geographicdatabase 116 as link, segment, or node attributes.

In order to provide navigation-related features and functions to the enduser, the mapping system 121 uses the geographic database 116. Thegeographic database 116 includes information about one or moregeographic regions. FIG. 2 illustrates a map 198 of a geographic region202. The geographic region 202 may correspond to a metropolitan or ruralarea, a state, a country, or combinations thereof, or any other area.Located in the geographic region 202 are physical geographic features,such as roads, points of interest (including businesses, municipalfacilities, etc.), lakes, rivers, railroads, municipalities, etc.

FIG. 2 further depicts an enlarged map 199 of a portion 206 of thegeographic region 202. The enlarged map 199 illustrates part of a roadnetwork 208 in the geographic region 202. The road network 208 includes,among other things, roads and intersections located in the geographicregion 202. As shown in the portion 206, each road in the geographicregion 202 is composed of one or more road segments 210. A road segment210 represents a portion of the road. Each road segment 210 is shown tohave associated with it two nodes 212; one node represents the point atone end of the road segment and the other node represents the point atthe other end of the road segment. The node 212 at either end of a roadsegment 210 may correspond to a location at which the road meets anotherroad, i.e., an intersection, or where the road dead-ends.

Referring to FIG. 3, in one embodiment, the geographic database 116contains data 302 that represents some of the physical geographicfeatures in the geographic region 202 depicted in FIG. 2. The data 302contained in the geographic database 116 may include data that representthe road network 208. In the embodiment of FIG. 3, the geographicdatabase 116 that represents the geographic region 202 may contain atleast one road segment database record 304 (also referred to as “entity”or “entry”) for each road segment 210 in the geographic region 202. Thegeographic database 116 that represents the geographic region 202 mayalso include a node database record 306 (or “entity” or “entry”) foreach node 212 in the geographic region 202. The terms “nodes” and“segments” represent only one terminology for describing these physicalgeographic features, and other terminology for describing these featuresis intended to be encompassed within the scope of these concepts.

The geographic database 116 may also include other kinds of data 312.The other kinds of data 312 may represent other kinds of geographicfeatures or anything else. The other kinds of data may include point ofinterest data. For example, the point of interest data may include pointof interest records comprising a type (e.g., the type of point ofinterest, such as restaurant, hotel, city hall, police station,historical marker, ATM, golf course, etc.), location of the point ofinterest, a phone number, hours of operation, etc. The geographicdatabase 116 also includes indexes 314. The indexes 314 may includevarious types of indexes that relate the different types of data to eachother or that relate to other aspects of the data contained in thegeographic database 116. For example, the indexes 314 may relate thenodes in the node data records 306 with the end points of a road segmentin the road segment data records 304. As another example, the indexes314 may relate point of interest data in the other data records 312 witha road segment in the segment data records 304.

FIG. 4 shows some of the components of a road segment data record 304contained in the geographic database 116 according to one embodiment.The road segment data record 304 may include a segment ID 304(1) bywhich the data record can be identified in the geographic database 116.Each road segment data record 304 may have associated with itinformation (such as “attributes”, “fields”, etc.) that describesfeatures of the represented road segment. The road segment data record304 may include data 304(2) that indicate the restrictions, if any, onthe direction of vehicular travel permitted on the represented roadsegment. The road segment data record 304 may include data 304(3) thatindicate a speed limit or speed category (i.e., the maximum permittedvehicular speed of travel) on the represented road segment. The roadsegment data record 304 may also include data 304(4) indicating whetherthe represented road segment is part of a controlled access road (suchas an expressway), a ramp to a controlled access road, a bridge, atunnel, a toll road, a ferry, and so on.

Data for high beams, low beams, fog lights, other vehicle lighting andillumination may be stored as separate records 308, 310 or in roadsegment data records 304. The geographic database 116 may include roadsegment data records 304 (or data entities) that describe features suchas high beams 304(5) or roadway illumination estimations 304(6). Theestimated roadway illumination may be stored as a field or record usinga scale of values such as from 1 to 100 (1 being dark, 100 being fullylit) or based on a measurement scale such as lux or illuminance, orrange thereof. The estimated roadway illumination may be stored usingcategories such as low, medium, high. The estimated roadway illuminationmay be stored as a variable related to visibility. For example, awell-lit road may have visibility up to 1000 feet or more. A medium litroad may have visibility of 500 feet to 1000 feet and a low visibilityroad may have visibility less than 500 feet. With high beams, a normalobserver may see from about 350-500 feet depending on the conditions. Alow illumination roadway may indicate visibility below what wouldnormally be a safe stopping distance. Additional schema may be used todescribe the estimated roadway illumination. The geographic database 116may store other data 312 relating to illumination such as high beamdata, headlight data, or actual ambient light readings. The attributedata may be stored in relation to a link/segment 304, a node 306, astrand of links, an area, or a region. The geographic database 116 maystore information or settings for display preferences. The geographicdatabase 116 may be coupled to a display. The display may be configuredto display the roadway network and data entities using different colorsor schemes. The geographic database 116 may store information relatingto where hazardous conditions may exist, for example, though analysis ofthe data records and current/historical traffic conditions. Roadsegments with low illumination may be used to identify or supplementother data entities such as potential hazards. High beam usage datarecords along with geographic data records may indicate through acombination of conditions that location on a roadway is not safe.

The road segment data record 304 also includes data 304(7) providing thegeographic coordinates (e.g., the latitude and longitude) of the endpoints of the represented road segment. In one embodiment, the data304(7) are references to the node data records 306 that represent thenodes corresponding to the end points of the represented road segment.

The road segment data record 304 may also include or be associated withother data 304(7) that refer to various other attributes of therepresented road segment. The various attributes associated with a roadsegment may be included in a single road segment record, or may beincluded in more than one type of record which cross-references to eachother. For example, the road segment data record 304 may include dataidentifying what turn restrictions exist at each of the nodes whichcorrespond to intersections at the ends of the road portion representedby the road segment, the name or names by which the represented roadsegment is known, the street address ranges along the represented roadsegment, and so on.

FIG. 4 also shows some of the components of a node data record 306 whichmay be contained in the geographic database 116. Each of the node datarecords 306 may have associated information (such as “attributes”,“fields”, etc.) that allows identification of the road segment(s) thatconnect to it and/or it's geographic position (e.g., its latitude andlongitude coordinates). For the embodiment shown in FIG. 4, the nodedata records 306(1) and 306(2) include the latitude and longitudecoordinates 306(1)(1) and 306(2)(1) for their node. The node datarecords 306(1) and 306(2) may also include other data 306(1)(3) and306(2)(3) that refer to various other attributes of the nodes.

The geographic database 116 may be maintained by a content provider(e.g., a map developer). By way of example, the map developer maycollect geographic data to generate and enhance the geographic database116. The map developer may obtain data from sources, such as businesses,municipalities or respective geographic authorities. In addition, themap developer may employ field personnel to travel throughout thegeographic region to observe features and/or record information aboutthe roadway. Remote sensing, such as aerial or satellite photography,may be used. The database 116 is connected to the server 125.

The geographic database 116 and the data stored within the geographicdatabase 116 may be licensed or delivered on-demand. Other navigationalservices or traffic server providers may access the traffic data and theestimated roadway illumination data stored in the geographic database116. Data including the estimated roadway illumination data for a linkmay be broadcast as a service.

The server 125 may be a host for a website or web service such as amapping service and/or a navigation service. The mapping service mayprovide maps generated from the geographic data of the database 116, andthe navigation service may generate routing or other directions from thegeographic data of the database 116. The mapping service may alsoprovide information generated from attribute data included in thedatabase 116. The server 125 may also provide historical, future, recentor current traffic conditions for the links, segments, paths, or routesusing historical, recent, or real time collected data. The server 125may be configured to analyze collected ambient light data or high beamdata to determine an estimated roadway illumination for segments orlinks. The server 125 may be configured to analyze data from segmentsand links to determine correlations between similar types of segmentsand nodes. For example, segments with similar high beam use may havesimilar accident profiles or traffic patterns.

The server 125 is connected to the network 127. The server 125 mayreceive or transmit data through the network 127. The server 125 mayalso transmit paths, routes, or estimated roadway illumination datathrough the network 127. The network 127 may include wired networks,wireless networks, or combinations thereof. The wireless network may bea cellular telephone network, LTE (Long-Term Evolution), 4G LTE, awireless local area network, such as an 802.11, 802.16, 802.20, WiMax(Worldwide Interoperability for Microwave Access) network, or wirelessshort range network. Further, the network 127 may be a public network,such as the Internet, a private network, such as an intranet, orcombinations thereof, and may utilize a variety of networking protocolsnow available or later developed including, but not limited totransmission control protocol/internet protocol (TCP/IP) basednetworking protocols.

The one or more devices 122 may include probe devices, probe sensors, orother devices 122 such as personal navigation devices 122 or connectedvehicles. The server 125 may communicate with the devices 122 throughthe network 127. The server 125 may also receive data from one or moresystems or services that may be used to predict roadway illumination.The devices 122 may be a mobile device or a tracking device thatprovides samples of data for the location of a person or vehicle. Thedevices 122 may include mobile phones running specialized applicationsthat collect location data as the devices 122 are carried by persons orthings traveling the roadway system. The devices 122 may also beintegrated in or with a vehicle. The devices 122 may be configured tocollect and transmit data including headlight and high beam usage. Thedevices 122 may be configured to collect and transmit data includingdata from light sensors on or about a vehicle.

Sensor data may be collected with a light sensor, or collection ofsensors, such as an optical detector (e.g., camera, light detection andranging (LiDAR), or radar device). The devices 122 and/or othersensor(s) may report the quantity, frequency, and/or speed of vehiclesas the devices 122 travel roadways. The devices 122 and/or othersensor(s) may report the level, frequency, and variances of illuminationas the devices 122 travel roadways. The road segment or link may bedetermined based on the geographical coordinates of the probe (e.g.,global positioning system (GPS)).

FIG. 5 illustrates an example flow chart for mapping road illumination.As presented in the following sections, the acts may be performed usingany combination of the components indicated in FIG. 1, FIG. 7, or FIG.10. The following acts may be performed by the server 125, the device122, the mapping system 121, or a combination thereof. Additional,different, or fewer acts may be provided. The acts are performed in theorder shown or other orders. The acts may also be repeated. Certain actsmay be skipped.

At act A110, the server 125 receives high beam data. High beam data maybe received over the network. High beam data may be collected by adevice 122 during operation of a vehicle. A device coupled with thevehicle may detect activation of the high beam switch or a sensor notdirectly connected but which detects the increase in brightness of theheadlights when the high beams are turned on. High beam data may becollected and transmitted, e.g. wirelessly, in real time and/or may bestored locally in the device 122 and transmitted at a later time to theserver 125. In situations where there is connectivity, the high beamdata may be relayed in real time but when connectivity drops, the devicemay store data until such time as connectivity is reestablished. Highbeam data may include data related to a high beam event such as timeactive, duration, and location data (which may be provided by anavigation system).

High beams are typically used on highways and rural roads without muchtraffic. High beams may be turned off if there is on-coming traffic or avehicle directly ahead. High beams may be operated manually by theoperator of a vehicle or automatically though usage of automatic highbeam systems. High beam data may be received as an ON or OFF state for aspecific time and location. For vehicles with more complex headlightsystems, additional data may be received such as level of brightness ordirection. As high beams may be turned off or on frequently, multiple ONand OFF states may exist for a particular road segment. Data may bestored for both the ON or OFF states.

FIG. 6 illustrates high beam data received over a period of time while avehicle has traveled from point A to F. Two states for the high beamdata exist (ON and OFF). As shown, the high beams are switched on atvarious time for different periods of time covering different roadsegments. For example, the high beams are in the ON state at around18:20 when the vehicle begins to traverse the C to D segment. The highbeams are momentarily turned to the OFF State at 18:55 then turned backON again at 19:00. A high beam event starts when the high beams aremoved to the ON state and ends when the high beams are turned OFF.Events may be bifurcated when a vehicle travels from one segment toanother. For example, an event that straddles the D node may be splitinto two separate events; one event before D, one event after D.

In certain embodiments, high beam data may only be received duringnon-daylight hours or when ambient light levels fall below a certainlevel. High beam usage during the day may not be useful as daylight highbeam usage may not relate to roadway illumination. Exceptions may existfor tunnels, heavily wooded areas, and long overpasses, among others.Daylight hours may include dawn and dusk, e.g. 30 minutes before sunriseand after sunset. Sunrise and sunset may be determined through use of atable and geographical location. Roadway location may also be used todetermine daylight hours. For example, a roadway in a valley may becomedarker sooner than one without any obstructions. For road segments thatare well travelled, high beam data may only be collected at the peak ofnight, e.g. the two darkest hours. Moon phases may also be used todetermine when high beam data is collected. Data collected during a fullmoon or closer to sunrise and sunset may include more lightcontamination and may not be indicative of normal illumination.

In certain embodiments, the server 125 may receive additional data suchas low beam data, fog light data, or headlight data from automatic oradaptive headlights. Headlights may be used manually or automatically.Manual headlights require an operator to turn on or off the headlightsor adjust brightness. Automatic headlights such as daytime runninglights may be turned on and off in response to vehicle operation orlight sensors. Automatic headlights may be activated through aphotoelectric sensor. The sensitivity of the sensor may be set by theauto manufacturer or the operator. The sensor may be activated by thelighting conditions such as at dawn or dusk. Some vehicles may also havea light sensor that informs the vehicle if the exterior is dark (e.g.night or in a tunnel). When the switches in the vehicle are set forautomatic headlights, the vehicle will turn on the lights whenever thesensor senses that the exterior is dark enough.

Automatic headlights may also detect nearby light sources such as theheadlights or tail lights of vehicles ahead. The system mayautomatically switch between high beams and low beams to ensure optimumnighttime visibility. High beams may be automatically activated when thefollowing conditions are met: the vehicle speed meets a threshold speed,for example fifteen miles per hour or more; the area in front of vehicleis dark; no oncoming headlights; no tail lights in front of vehicle; nostrong lighting, from street lights, etc., ahead of vehicle. Automaticheadlights may also be controlled by data received from a geographicdatabase 116 including road illumination data. For example, automaticheadlights may be limited to road segments that have poor or low ambientlighting.

When receiving high beam data, the server 125 may not differentiatebetween manual or automatic high beam events. However, the server 125may collect the data that initiates the automatic high beams todetermine if the high beam event is valid (e.g. speed and light sensordata). In vehicles where automatic high beams are turned off, the server125 may still receive data that the high beams would have been turned onhad the option not been disabled. For vehicles that do not haveautomatic high beams, the server 125 may likewise receive a notificationthat the conditions exist (speed and light sensor data) that wouldprompt a high beam event. The server 125 may store OFF data as well asON data. The lack of high beam usage may be indicative of adequatelighting.

Other advanced systems such as adaptive headlights may illuminate aroundcorners by aiming the beam in the direction an operator has turned thesteering wheel. Adaptive headlights may use mechanical systems orelectronic systems. In certain situations, if the speed of a vehicle isincreased, the lights are raised automatically to provide morevisibility. If the vehicle slows down, signals, or comes upon traffic inthe opposite lane, the light beams may be automatically lowered.Adaptive headlights may also include an auto-off setting to help preventthe vehicle from “blinding” fellow drivers or leaving the lights on.When the vehicle is parked or idling with the wheel cranked toward theroad, the lights may be turned off to protect other drivers.

Manual, automatic, and adaptive headlights (low or high beam) maygenerate data that is collected by a device 122. Further data, such asuse of fog lights, low beam usage during daylight (or nighttime) hoursor other vehicle lighting data may be generated and collected. Foglights, for example, throw out a low, wide beam which cuts below the fogto help you see where the edges of the road are. Fog lights may beturned on automatically or manually. Fog lights may be rear or frontfacing. Additional data, such as the sensor data (light, speed,direction) that determines how the automatic and adaptive headlightswork may also be collected. This data may be received either in act A110or act A120 below. The server 125 may receive all of this data either inreal time or the data may be stored locally until transmission ispossible or efficient.

At act A120, the server 125 receives roadway condition data. Roadwayconditions may include additional data collected by vehicle sensors orthe device 122. Roadway conditions may also include data received from atraffic management center or other service. Roadway data may includetraffic or weather data.

The roadway data may be existing data regarding the physical structureor layout of a location or road segment. For example, a geographicdatabase 116 may contain data for each segment including such aspects asnumber of lanes, road width, road curvature, grade, barriers, and speedlimit, among others.

Traffic data may include data relating to other vehicles. Traffic datamay include traffic volume, traffic flow, average speed, accidents,among others. Traffic data may be collected by the device 122 orreceived from a traffic management center. Weather data may includelocal weather conditions such as precipitation, cloud cover, andexpected visibility among others. Weather data may be collected from thevehicle or from a weather center.

At act A130, the server 125 calculates a high beam confidence valuebased on the high beam data and the roadway conditions. The high beamconfidence value may indicate a level of confidence that the high beamevent occurred in response to low levels of roadway illumination. Falsepositives, or events where a high beam event is detected but the highbeams are not intended for low levels of light, may be considered noisefor determining low roadway illumination. For example, some drivers mayuse a flash feature on a high beam control arm to alert a drivertraveling too slowly in the passing lane. Drivers may also forget toturn off high beams even when no longer needed, such as transitioningfrom a rural area to a well-lit street in town. Drivers may use highbeams during certain types of weather. Drivers may also accidently turnon high beams. Each of the events may need to be excluded or discountedto accurately determine roadway illumination. Such events may be seen inthe example data from FIG. 6 described above. The first event (E1) inFIG. 6 takes place prior to sunset (in this example). As such, thisevent is most likely not going to be useful. The second event (E2) is avery short duration. This is most likely a driver attempting to alert afellow driver. The fourth event (E4) is similar and potentially anattempt to alert a fellow driver.

A high beam confidence value may be calculated using the high beam dataand the roadway conditions. The confidence value may be numerical orencompass varying levels or categories. The value may be comparedagainst a confidence threshold in order to exclude data that is notconsidered useful to determine roadway illumination. Table 1 listsseveral different high beam events and associated roadway data (for thetime and location). For these examples, each event occurs at night(daytime events have been pre-culled from the dataset). Additional datafrom the data shown may be used to determine a high beam confidencevalue. A high beam event may include the high beams being in the ONstate. Events may be bifurcated when a vehicle transitions from a firstsegment to a second segment. The first event ends at the node betweensegments, the second event starts at the node.

TABLE 1 Event Duration Type of Traffic Speed Id Segment (sec) RoadWeather Volume (mph) A 10001 4 City Clear Heavy 20 B 10002 45 RuralClear medium 45 C 10002 24 Rural Clear medium 45 D 10002 22 Rural Clearmedium 45 E 10003 134 Rural Rain light 35 F 10003 256 Rural Rain light35 G 10006 2 Rural Fog light 22 H 10006 72 Rural Clear light 55 I 100092 City Clear medium 45 J 10013 678 Rural Snow light 12 K 10013 897 RuralSnow light 2

For this example, there are three categories for to place data into: Lowprobability, medium probability, and high probability. Each categoryrepresents the likelihood that data is useful for determining roadwayillumination or high beam frequency. Low probability events may bediscounted compared to high probability events when aggregating multipleevents. In certain embodiments, there may be additional categoriesdepending on the amount and granularity of data. For example, withadditional data, the server 125 may be able to perform a more complexsorting.

Event ID (A) takes place on segment 10001 and lasts for 4 seconds. Theroad type is city, there are no weather conditions, and the trafficvolume is heavy. The vehicle is traveling at approx. 20 mph. The server125 may interpret the short duration as indicating a low probabilityevent. Likewise for the type of road and traffic volume. Both indicatorssuggest that the high beam event would be unlikely to be useful fordetermining roadway illumination. This event possibly indicates a driversignaling to a different vehicle.

Event ID's (B), (C), and (D) all take place on segment 10002. Thedurations 45 s, 24 s, and 22 s all are indicators that this may be ahigh probability event. Likewise, the setting (rural), the trafficvolume (medium), and the average speed indicate that the events werelikely to be useful for determining roadway illumination. Additionalinformation, such as the time between events or the total time for thevehicle to traverse the segment may be beneficial to the determination.

Event ID's (E) and (F) involve long durations on rural roads. Both arehigh probability events. Event ID (I) is a low probability event due tothe duration and location.

Event ID's (G) and (H) both take place on segment 10006. (G) is of ashort duration, while (H) is much longer. Because (G) is short, (G)would normally indicate a low probability event. (H) as a long durationevent on a rural road may indicate a high probability event. (G) as alow probability event may not be used to determine the roadwayillumination.

Event ID's (J) and (K) both are long duration events that take place inrural areas. However due to the weather (snow) and speed (slow), bothevent are suspect. One possibility is that due to the weather conditionsand visibility, the driver was using high beams to make up for thefailure of the overhead lighting to illuminate the roadway. Both (J) and(K) may be determined to be medium probability events.

At act A140, the server 125 calculates a high beam frequency for asegment. The server 125 may aggregate a plurality of high beamconfidence values to determine a frequency estimate. The server 125 mayfurther calculate a roadway illumination value from the high beamfrequency or the high beam confidence values. For each segment there mayexist one or more high beam confidence values covering a specific time.For example, for segment 10002 in Table 1 shown above there are threeevents. Each time the high beams are in the ON position may beconsidered a separate event. Each event may have a high beam attributecalculated above at act 130. For segment 10002, the three events are allhigh probability events. The server 125 may determine from the threeevents that there is a high likelihood that there is high beam usage andlow illumination for the segment 10002. The server 125 may aggregatemultiple events from different sources. For example, certain operatorsmay be more or less likely to use high beams. Data from one vehicle maybe very useful and as such weighted higher, whereas lower value data(even though it is deemed positive) may be grouped together and weightedless. Low probability events may be used to determine a roadillumination estimate; however, low probability events may be weightedlower or discounted compared to high probability events.

In certain embodiments, the ON high beam events may be aggregated firstby the source of the events. The reports for a vehicle may first begrouped together to ascertain the condition of a road segment at aparticular time. Reports for the same types of vehicles may beaggregated together (sports cars/trucks/buses/mini vans/sedans etc.)Reports for similar traffic conditions or weather conditions mayaggregated together. Reports from the same time period may be aggregatedtogether. In the example described above, all three events from segment10002 may be aggregated together prior to being combined with other datafrom other devices.

Once the server 125 has aggregated and sorted each event, the server 125may determine an estimation of road illumination. The server 125 may usethe volume of traffic for a segment to more accurately calculate roadillumination. For example, the number of high probability events is adirect indictor of road illumination. A high number would indicate lowillumination, and vice versa. However, on a rural road with very littletraffic (particularly during night time hours) there may only be a fewevents compared to a medium traffic highway. A high traffic highway maysee a decrease in high beam events due to the number of cars andpotentially slower speeds. The number of events may be compared to thevolume of traffic for a roadway, an area, or a region.

At act A150, the server 125 generates or augments a geographic database116. The geographic database 116 may include link and node data withassociated attributes such as the estimated roadway illumination data.The geographic database 116 may also contain the high beam attributesand the raw high beam data. The generated or augmented geographicdatabase 116 may be part of a larger geographic database 116 asdescribed above. The geographic database 116 may be used to generate ordisplay a visual map of an area or region. Different colors orindicators may be used to indicate the levels of roadway illuminationfor a particular segment. The geographic database 116 may be used in alayering application, wherein a user may toggle on or off the roadwayillumination estimation data.

The server 125 may generate a route using the high beam data in thegeographic database 116 or display high beam usage or roadwayillumination. The server 125 may publish high beam usage or roadwayillumination using a traffic management channel (TMC), or throughintelligent transportation systems (ITS), or other broadcast system orschema. The geographic database 116 may be used with real-time data toestimate local conditions and to generate operating instructions orsuggestions for a vehicle. For example, the server 125 may receive arequest for a well illuminated route. A request may contain a thresholdillumination. A request may include a visibility threshold. The server125 may calculate visibility for a road segment based on the roadwayconditions, weather conditions, and/or an estimated road illumination.The server 125 may generate multiple routes including different optionsfor both time, types of road segments, and illumination. One route maybe limited to only using road segments that have a high likelihood ofbeing well lit. Another route may additionally use road segments thathave a medium likelihood of being well lit. Each route may also begenerated with current or expected traffic conditions to give anestimated time of arrival. The route may be transmitted to a device 122.The route may be displayed using a geographic database 116.

FIG. 7 illustrates an example server 125 of the system of FIG. 1. Theserver 125 includes a processor 300 that is connected to acommunications interface 305 and a memory 301. The processor 300 is alsoconnected to the database 116. The communications interface 305 isconfigured to receive headlight data from one or more probes or devices122. The memory 301 is configured to store received real-time andhistorical data. The processor is configured to estimate roadwayillumination levels for a road segment. The processor may be configuredto generate maps and routing instructions. Additional, different, orfewer components may be included.

FIG. 8 illustrates an example workflow for estimating roadwayillumination using the server 125 of FIG. 7. As presented in thefollowing sections, the acts may be performed using any combination ofthe components indicated in FIG. 1, FIG. 7, or FIG. 10. The followingacts may be performed by the server 125, the device 122, or acombination thereof. Additional, different, or fewer acts may beprovided. The acts are performed in the order shown or other orders. Theacts may also be repeated.

At act A210, the server 125 receives information from a device 122 aboutheadlight settings, in particular a driver's use of high beams. The highbeam data may include both the state of the high beams (ON/OFF), alocation, and a time. The server 125 may receive high beam data thatindicates an ON state and a duration of the ON state. The server 125 mayreceive additional information from the device 122 related to theheadlight settings. Headlight systems such as automatic or adaptivesystems may report additional information that may be transmitted to theserver 125 though the communications interface. For example, if theoperator overrode the operation of an automated high beam system. Thecommunication interface 205 and/or communication interface 305 mayinclude any operable connection. An operable connection may be one inwhich signals, physical communications, and/or logical communicationsmay be sent and/or received. An operable connection may include aphysical interface, an electrical interface, and/or a data interface.The communication interface 205 and/or communication interface 805provides for wireless and/or wired communications in any now known orlater developed format.

At act A220, the server 125 receives ambient light data. Ambient lightsensors may directly measure the amount of light at a time and location.Ambient light sensors may be similar to the sensors used to enableautomatic headlights. The light data from ambient light sensors may pickup not only overhead lighting but also other sources such as passingvehicles or onetime events.

At act A230, the server 125 calculates a high beam frequency for one ormore road segments based on the high beam data. The server 125 mayaggregate multiple high beam events for each segment to determine a highbeam frequency. The high beam frequency may be calculated by determininghow often high beams are used compared to traffic volumes on the roadsegment. Due to noise, having multiple sets of data from multiplevehicles at different time, may assist in excluding high beam data thatis not useful.

At act A240, the server 125 calculates an ambient light value for theone or more road segments based on the ambient light data. FIGS. 9A and9B illustrates two examples received of ambient light levels. FIG. 9Aillustrates the signal from the ambient road lighting. For roads withoverhead lighting, the effective ambient lighting will be reasonablyuniform, and fluctuations will have a fairly long period as a functionof the car's position. For example, in FIG. 9A as the vehicle moves awayfrom a first overhead light 710, the ambient light gradually diminishesuntil the vehicle is midway between overhead lights. As the vehiclemoves towards the second overhead light 715, the ambient light graduallyincreases until the vehicle is at the closest point to the secondoverhead light 715. This pattern of gradually increasing and decreasingmay be evident even when the overhead lighting is not equally spaced.

FIG. 9B illustrates expected ambient light readings from a vehicle 720passing in the opposite direction. The signal from the sensor will risemuch more quickly (See 730 and the slope of the rise) than the exampleshown in FIG. 9A above. Once the vehicle has passed the sensors, theambient light drops sharply (See 740 where the ambient light falls offquickly). The server 125 may use Fourier analysis to separate the highand low frequency components in order to determine patterns. Additionalsources of ambient light may be detected by the sensors. For example,other roadside sources such as businesses or houses may affect thecollection of overhead lighting. Other transient/temporary illuminationsource such as from a bicycle, portable lights, overhead helicopter,lighted signs, or flares, among others may also be detected andexcluded. Lighting from behind the vehicle, may not be an issue as theambient light sensor is typically mounted towards the front of thevehicle and as such other vehicles driving behind our vehicle will notbe a large source of contamination. For a given segment of road, theserver 125 may require that there be data from multiple vehicles so thatthe server 125 may form a distribution of ambient light values. The datafrom the vehicles may be collected at the same time or different timesincluding different days. The data may be grouped together with similardata (day of the week, time of day, seasonality, weather conditions).The data may further be grouped by types of vehicles, driver data(possibly derived from insurance information or a driving record). Theminimum value of the distribution may then be associated with the trueambient light, while higher values would likely be due to contaminationfrom other nearby vehicles and sources.

At act A250, the server 125 generates an estimated road illuminationvalue for the one or more road segments based on the high beam frequencyand the ambient light value. Table 2 shown below illustrates a matrix oflight sensor readings (ambient light value) and high beam frequency.From the data values, the server 125 may calculate an estimated roadillumination value and a confidence in the estimate.

TABLE 2 Estimated road Light sensor High beam use illuminationConfidence in reading frequency level estimate Low Never Low Low MediumNever Medium Medium High Never Good High Low Sometimes Low High MediumSometimes Low Medium High Sometimes Medium Low Low Often Low High MediumOften Low Medium High Often Medium Low

Most roads with overhead lighting will be represented by the datasetincluding high light sensor readings and high beam use frequency ofnever. The illumination for the roads may be estimated to be good.Likewise rural roads without overhead lighting will may be representedby the row containing low light sensor readings and a high beam usefrequency of often. Certain combinations such as (Low|Never)(High|Sometimes) and (High|Often) are unlikely to occur.

The server 125 may further generate, augment, or supplement a geographicdatabase 116 with the estimated road illumination level. The server 125may publish the estimated road illumination level over a network or on acomputer readable medium. The estimated road illumination level may bestored in memory as a record or attribute for each segment. The lightsensor reading and the high beam use frequency may further be stored asattributes. The geographic database 116 may be used in route generationor as a mapping service. Route generation may be informed by theestimated road illumination attribute. For example, routes may begenerated to avoid poorly lit area. The geographic database 116 may betransmitted or downloaded to a device 122.

The estimated road illumination and confidence values may be correlatedwith other information or attributes in the geographic database 116. Forexample, high beam usage on road segments may correlate with highincident (or accident) segments. Low beam usage during daytime hours mayindicate low illumination conditions. Fog lights may indicate weatherconditions. Additionally, the lack of usage for headlights (low or high)may be correlated with other information. The correlation of use ornon-use may be used to inform drivers or autonomous vehicles to eitheravoid the segments or to be aware of the increased risks.

The estimated road illumination and confidence values may be used toautomatically control headlight settings. Automatic headlights mayrequire several prerequisites to determine when to turn on or off. Theestimated road illumination attributes may be used as an input indetermining when to automatically turn on or turn off (orincrease/decrease brightness) the headlights or high beams. Theestimated road illumination attribute may also provide notifications tomanually operated vehicles or autonomous vehicles. For a manuallyoperated vehicle, when the vehicle enters a low lit segment, an alertmay be generated to inform the driver that high beams may be needed. Analert may be provided though visually or audio stimulation. For anautonomous vehicle, when the vehicle enters a low lit segment, thesystem may require the vehicle to switch from autonomous to manual. Thesystem may further set a lower speed for the vehicle.

FIG. 10 illustrates an example device 122 of the system of FIG. 1. Thedevice 122 may be configured to collect, transmit, receive, process, ordisplay data. The device 122 may also be referred to as a probe 122, amobile device 122 or a navigation device 122. The navigation device 122includes a controller 200, a memory 204, an input device 203, acommunication interface 205, position circuitry 207, movement circuitry208, and an output interface 211. The output interface 211 may presentvisual or non-visual information such as audio information. Additional,different, or fewer components are possible for the mobile device 122.The navigation device 122 may be smart phone, a mobile phone, a personaldigital assistant (PDA), a tablet computer, a notebook computer, apersonal navigation device (PND), a portable navigation device, and/orany other known or later developed mobile device. In an embodiment, avehicle may be considered a device 122, or the device 122 may beintegrated into a vehicle. The device 122 may receive or collect datafrom one or more sensors in or on the vehicle.

The device 122 may be configured to execute routing algorithms using ageographic database 116 to determine an optimum route to travel along aroad network from an origin location to a destination location in ageographic region. Using input from an end user, the device 122 examinespotential routes between the origin location and the destinationlocation to determine the optimum route in light of user preferences orparameters. The device 122 may then provide the end user withinformation about the optimum route in the form of guidance thatidentifies the maneuvers required to be taken by the end user to travelfrom the origin to the destination location. Some devices 122 showdetailed maps on displays outlining the route, the types of maneuvers tobe taken at various locations along the route, locations of certaintypes of features, and so on.

The navigation device 122 is configured to identify a starting locationand a destination. The starting location and destination may beidentified though the input device 203. The input device 203 may be oneor more buttons, keypad, keyboard, mouse, stylus pen, trackball, rockerswitch, touch pad, voice recognition circuit, or other device orcomponent for inputting data to the mobile device 122. The input device203 and the output interface 211 may be combined as a touch screen thatmay be capacitive or resistive. The output interface 211 may be a liquidcrystal display (LCD) panel, light emitting diode (LED) screen, thinfilm transistor screen, or another type of display. The output interface211 may also include audio capabilities, or speakers.

The starting location (such as a current location) may be identifiedusing positional circuitry such as GPS or other positional inputs. Thepositioning circuitry 207, which is an example of a positioning system,is configured to determine a geographic position of the navigationdevice 122. The movement circuitry 208, which is an example a movementtracking system, is configured to determine movement of a navigationdevice 122. The position circuitry 207 and the movement circuitry 208may be separate systems, or segments of the same positioning or movementcircuitry system. In an embodiment, components as described herein withrespect to the navigation device 122 may be implemented as a staticdevice. For example, such a device may not include movement circuitry208, but may involve a speed or velocity detecting input device 203.

The navigation device 122 may identify its position as the devicetravels along a route using the positional circuitry. For indoor spaceswithout GPS signals, the navigation device 122 may rely on othergeolocations methods such as LIDAR, radar, Wi-Fi, beacons, landmarkidentification, inertial navigation (dead reckoning), among others.

The navigation device 122 is further configured to request a route fromthe starting location to the destination. The navigation device 122 mayfurther request preferences for the route including road illuminationlevels. The communication interface 205 and/or communication interface305 may include any operable connection. An operable connection may beone in which signals, physical communications, and/or logicalcommunications may be sent and/or received. An operable connection mayinclude a physical interface, an electrical interface, and/or a datainterface. The communication interface 205 and/or communicationinterface 305 provides for wireless and/or wired communications in anynow known or later developed format. The communication interface 205and/or communication interface 305 may include a receiver/transmitterfor digital radio signals or other broadcast mediums. Areceiver/transmitter may be externally located from the device 122 suchas in or on a vehicle.

The navigation device 122 is configured to receive the route. The routemay be generated from a geographic database 116. The route may includeroad illumination estimates in the form of attributes for road segments,links, or nodes. Depending on the preferences and the request, the routemay only contain segments that meet a threshold for illumination. Theroute may include notifications for segments that are not well lit. Theroute may include notifications for areas or segment that have frequenthigh beam usage.

The route may be displayed using the output interface 211. The route maybe displayed for example as a top down view or as an isometricprojection. Road segments may be colored or shaded differently dependingon the road segment's estimated illumination. For example, a well-litroadway may be highlighted in yellow, while a low lit side road may be ashade of grey. While relative and/or subject terminology may be usedthroughout to describe levels of illumination, it will be appreciatedthat the use of such terminology is for convenience and that one ofordinary skill in the art would appreciate that the adequacy ofillumination is dependent on the application or implementation of adevice dependent thereon, such as light sensitivity, and that what maybe considered “well lit” for one application may be poorly lit foranother. However, in all such situations the disclosed embodiments maybe utilized to objectively map illumination levels at various areas in amanner which may then be evaluated and utilized based on the intendedapplication. The navigation device may store both the route and data forthe surrounding area in a local memory.

The route may be generated with additional information from a geographicdatabase 116. Routes for pedestrians or cyclists (who may preferwell-lit roads for safety reasons), may be generated specifically forthose modes of transportation. Pedestrian routes may further be limitedto well trafficked, well-lit roadways. Cyclists may prefer low traffic,well-lit roadways.

The memory 204 and/or memory 801 may be a volatile memory or anon-volatile memory. The memory 204 and/or memory 801 may include one ormore of a read only memory (ROM), random access memory (RAM), a flashmemory, an electronic erasable program read only memory (EEPROM), orother type of memory. The memory 204 and/or memory 801 may be removablefrom the mobile device 122, such as a secure digital (SD) memory card.The memory may contain a locally stored geographic database or link noderouting graph. The locally stored geographic database may be a copy ofthe geographic database or may include a smaller piece. The locallystored geographic database may use the same formatting and scheme as thegeographic database. The navigation device 122 may determine a route orpath from a received or locally geographic database using the controller200. The controller 200 and/or processor 300 may include a generalprocessor, digital signal processor, an application specific integratedcircuit (ASIC), field programmable gate array (FPGA), analog circuit,digital circuit, combinations thereof, or other now known or laterdeveloped processor. The controller 200 and/or processor 300 may be asingle device or combinations of devices, such as associated with anetwork, distributed processing, or cloud computing. The controller 200may also include a decoder used to decode roadway messages and roadwaylocations.

The estimated road illumination may be used to directly or indirectlynavigate a vehicle. The device 122 may be integrated into an autonomousvehicle or a highly assisted driving (HAD) vehicle. The device 122 maybe configured as a navigation system for an autonomous vehicle or a HAD.An autonomous vehicle or HAD may take route instruction based on thelink and node information provided to the navigation device 122.

As described herein, an autonomous driving vehicle may refer to aself-driving or driverless mode that no passengers are required to be onboard to operate the vehicle. An autonomous driving vehicle may bereferred to as a robot vehicle or an autonomous driving vehicle. Theautonomous driving vehicle may include passengers, but no driver isnecessary. Autonomous driving vehicles may park themselves or move cargobetween locations without a human operator. Autonomous driving vehiclesmay include multiple modes and transition between the modes.

As described herein, a highly assisted driving (HAD) vehicle may referto a vehicle that does not completely replace the human operator.Instead, in a highly assisted driving mode, the vehicle may perform somedriving functions and the human operator may perform some drivingfunctions. Vehicles may also be driven in a manual mode that the humanoperator exercises a degree of control over the movement of the vehicle.The vehicles may also include a completely driverless mode. Other levelsof automation are possible.

The autonomous or highly automated driving vehicle may include sensorsfor identifying the surrounding and location of the car. The sensors mayinclude GPS, light detection and ranging (LIDAR), radar, and cameras forcomputer vision. Proximity sensors may aid in parking the vehicle. Theproximity sensors may detect the curb or adjacent vehicles. Theautonomous or highly automated driving vehicle may optically track andfollow lane markings or guide markings on the road. For an autonomousvehicle, the maximum speed may be limited by the range of the vehiclesensors. Although sensor technologies such as radar and ultrasound areunaffected by visible sight range, video cameras require adequate lightto function. Video cameras are used for important tasks such as thedetection of Stop signs.

The term “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

As used in this application, the term ‘circuitry’ or ‘circuit’ refers toall of the following: (a) hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) and (b) tocombinations of circuits and software (and/or firmware), such as (asapplicable): (i) to a combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone or server, to perform various functions) and (c) tocircuits, such as a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation, evenif the software or firmware is not physically present.

This definition of ‘circuitry’ applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) or portionof a processor and its (or their) accompanying software and/or firmware.The term “circuitry” would also cover, for example and if applicable tothe particular claim element, a baseband integrated circuit orapplications processor integrated circuit for a mobile phone or asimilar integrated circuit in server, a cellular network device, orother network device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read only memory or arandom access memory or both. The essential elements of a computer are aprocessor for performing instructions and one or more memory devices forstoring instructions and data. Generally, a computer also includes, orbe operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio player, a Global Positioning System (GPS) receiver, to namejust a few. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., EPROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The memory may be a non-transitorymedium such as a ROM, RAM, flash memory, etc. The processor and thememory can be supplemented by, or incorporated in, special purpose logiccircuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, are apparent to those of skill in the artupon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention. The claims should not be read as limited to thedescribed order or elements unless stated to that effect. Therefore, allembodiments that come within the scope and spirit of the followingclaims and equivalents thereto are claimed as the invention.

1. (canceled)
 2. A method for providing navigation services, the methodcomprising: collecting high beam data by a first sensor for a roadsegment; collecting roadway condition data by a second sensor for theroad segment; generating a data message including the high beam data andthe roadway condition data for the road segment; and transmitting thedata message to a navigation service provider.
 3. The method of claim 1,wherein the roadway condition data comprises at least ambient lightdata.
 4. The method of claim 1, wherein the roadway condition datacomprises at least weather data for the road segment.
 5. The method ofclaim 1, wherein the roadway condition data comprises at least trafficdata.
 6. The method of claim 1, wherein the roadway condition datacomprises at least speed data.
 7. The method of claim 1, wherein thehigh beam data comprises a length of time that a high beam was active.8. The method of claim 1, wherein the high beam data comprises timeactive, duration, and location data for a high beam event.
 9. The methodof claim 1, wherein the first sensor and second sensor are integratedinto a vehicle.
 10. A computer-readable, non-transitory medium storing aprogram that causes a computer to execute a method comprising:collecting high beam data by a first sensor for a location on a roadsegment; collecting ambient light data by a second sensor for thelocation on the road segment; calculating, by a processor, a high beamconfidence value as a function of the high beam data and the ambientlight data; generating, by the processor, a data message including thehigh beam confidence value; and transmit the data message including thehigh beam confidence value to a navigation service provider.
 11. Themethod of claim 10, further comprising: comparing the high beamconfidence value against a confidence threshold; wherein the datamessage is generated when the confidence threshold is met or exceeded.12. The method of claim 10, wherein the high beam data and ambient lightdata is collected during a nighttime period of a day.
 13. The method ofclaim 10, wherein the high beam data comprises a length of time that ahigh beam was active.
 14. The method of claim 10, wherein the navigationservice provider is located in a cloud computing network.
 15. The methodof claim 10, further comprising: collecting weather data by a thirdsensor for the location on the road segment; wherein the high beamconfidence value is calculated as a function of the weather data. 16.The method of claim 10, further comprising: collecting traffic data by athird sensor for the location on the road segment; wherein the high beamconfidence value is calculated as a function of the traffic data.
 17. Anapparatus for providing navigation services, the apparatus comprising: afirst sensor configured to monitor a high beam event of a vehicle andgenerate high beam data; a processor configured to calculate a high beamconfidence value as a function of the high beam data; and a transmitterconfigured to transmit the high beam confidence value to a navigationservice provider.
 18. The apparatus of claim 17, further comprising: asecond sensor configured to monitor ambient light and to generateambient light data; wherein the ambient light data is used to calculatethe high beam confidence value.
 19. The apparatus of claim 17, whereinthe high beam data comprises time active, duration, and location datafor a high beam event.
 20. The apparatus of claim 17, wherein the highbeam confidence value indicates a level of confidence that the high beamevent occurred in response to a low level of roadway illumination.
 21. Amethod using high beam data to augment a geographic database, the methodcomprising: receiving a high beam confidence value, wherein the highbeam confidence value is indicative of whether a vehicle's high beamswere active on a low illumination road segment; calculating, by aprocessor, a high beam frequency for the road segment based on the highbeam confidence value; and augmenting, by the processor, the geographicdatabase by including the high beam frequency for the road segment.